Representation of biomedical sublanguages using symbolic notation John MacMullen SILS Bioinformatics Journal Club Fall 2003

Terminology article assumptions “Knowledge encoded in textual documents is organized around sets of domain-specific terms…” [938] “Terms represent the most important concepts in a domain and characterize documents semantically.” [939] “[T]he basic problem is to recognize domain-specific concepts and to extract instances of specific relationships among them.” [938] Terms are ambiguous and have variation; they are hardly ever mono-referential The lack of naming conventions (controlled vocabularies), the existence of acronyms, and the large existing heterogeneous literatures increase complexity. [from Nenadic, G., Spasic, I., & Ananiadou, S. (2003). Terminology-driven mining of biomedical literature. Bioinformatics 19(8), 938-943.] SILS Bioinformatics Journal Club – Fall 2003

SILS Bioinformatics Journal Club – Fall 2003 Harris’ Assumptions “[T]here is a particular structure to science information in general, and to the information of each subscience in particular”, [because] “for each subscience there are particular subsets of nouns that occur with particular subsets of verbs or other words” [215]. “[I]t is not intrinsic properties of sounds and meanings that determine the possible word-sequences of sentences.” […] “For each word, we find roughly stable inequalities of probability among the words in its required (positive probability) set” [216]. “What is common to the texts of a given subject matter is that first-level words of a given subset require zero-level words of only a particular subset” [217]. “We thus obtain for the science several statement-types […]” [217]. “What we have here is thus an information-theoretic approach to the structure of information, as against solely the amount of information” [217, emphasis added] SILS Bioinformatics Journal Club – Fall 2003

Linguistic probabilities “For each word, we find roughly stable inequalities of probability among the words in its required (positive probability) set” [216]. “The meaning of a word is indicated, and in part created, by the meanings of the words in respect to which it has higher than average probability” [217] “Words with highest probability in respect to another word, or which otherwise can be shown structurally to have highest expectancy, add little or no information” [217]. SILS Bioinformatics Journal Club – Fall 2003

Representing Meaning with Notation Movement towards structured rather than natural language Representing sentences as propositions whose truth can be tested Example [from 217-218]: If ‘G’ = “antigen”, and ‘J’ = “injected into”, and ‘B’ = “ear”, then ‘GJB’ = “antigen injected into ear” SILS Bioinformatics Journal Club – Fall 2003

Symbolic representation SILS Bioinformatics Journal Club – Fall 2003

SILS Bioinformatics Journal Club – Fall 2003 Applications Investigate “the possibilities of obtaining standard notations for science languages,not by fiat but by boiling down from actual use…” [220]. “[R]elate the information structure of a science to anything else that characterizes the field, in order to reach if possible a ‘‘structure’’of the science” [220]. “[S]ee how tabular or other two-dimensional displays can represent the data (or the Result statements) of articles, for human inspection or for computer processing” [220]. SILS Bioinformatics Journal Club – Fall 2003

SILS Bioinformatics Journal Club – Fall 2003 Other Propositions “[W]hen, in a given science, articles written in different languages are analyzed […], we obtain the same sentence-types and structures,with only small differences due to the languages. “The word class and subclass symbols,and the sentence-types,are therefore not just a sublanguage of a particular language, but an independent symbolic linguistic system” [219]. Difference between ‘equivalance’ and ‘equal’? Example: La casa di Gianni è bianco. [original] The house of Gianni is white. [literal or equal] John’s house is white. [equivalent] SILS Bioinformatics Journal Club – Fall 2003

SILS Bioinformatics Journal Club – Fall 2003 Questions Assume Harris’ notation method is valid and works well. How might it be implemented in practice? (This is both an algorithm question and a policy question.) Who would apply it? What would some of the barriers be? Do Harris' arguments hold in an interdisciplinary environment? SILS Bioinformatics Journal Club – Fall 2003

SILS Bioinformatics Journal Club – Fall 2003 References Harris, Zellig S. (2002). The structure of science information. Journal of Biomedical Informatics, 35, 215-221. Linguistic String Project @ NYU: http://www.cs.nyu.edu/cs/projects/lsp/ MedLEE project (Medical Language Extraction and Encoding System): http://cat.cpmc.columbia.edu/medleexml/ Zellig Harris homepage: http://www.dmi.columbia.edu/zellig/ SILS Bioinformatics Journal Club – Fall 2003